Construct Single-Hierarchical P/NBD Model for Long Timeframe Synthetic Data
In this workbook we construct the non-hierarchical P/NBD models on the synthetic data with the longer timeframe.
1 Load and Construct Datasets
We start by modelling the P/NBD model using our synthetic datasets before we try to model real-life data.
1.1 Load Long Time-frame Synthetic Data
We now want to load the short time-frame synthetic data.
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customer_cohortdata_tbl <- read_rds("data/synthdata_longframe_cohort_tbl.rds")
customer_cohortdata_tbl |> glimpse()Rows: 50,000
Columns: 4
$ customer_id <chr> "LFC201001_0001", "LFC201001_0002", "LFC201001_0003", "…
$ cohort_qtr <chr> "2010 Q1", "2010 Q1", "2010 Q1", "2010 Q1", "2010 Q1", …
$ cohort_ym <chr> "2010 01", "2010 01", "2010 01", "2010 01", "2010 01", …
$ first_tnx_date <date> 2010-01-01, 2010-01-01, 2010-01-01, 2010-01-01, 2010-0…Show code
customer_simparams_tbl <- read_rds("data/synthdata_longframe_simparams_tbl.rds")
customer_simparams_tbl |> glimpse()Rows: 50,000
Columns: 9
$ customer_id <chr> "LFC201001_0001", "LFC201001_0002", "LFC201001_0003", …
$ cohort_qtr <chr> "2010 Q1", "2010 Q1", "2010 Q1", "2010 Q1", "2010 Q1",…
$ cohort_ym <chr> "2010 01", "2010 01", "2010 01", "2010 01", "2010 01",…
$ first_tnx_date <date> 2010-01-01, 2010-01-01, 2010-01-01, 2010-01-01, 2010-…
$ customer_lambda <dbl> 0.110749068, 0.140668542, 0.107118406, 0.119591579, 0.…
$ customer_mu <dbl> 0.224078451, 0.074647615, 0.113866440, 0.054091413, 0.…
$ customer_tau <dbl> 12.6341383, 14.8118968, 0.8963641, 15.4056745, 4.29091…
$ customer_amtmn <dbl> 41.922469, 13.268255, 196.981298, 16.214932, 33.372405…
$ customer_amtcv <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …Show code
customer_transactions_tbl <- read_rds("data/synthdata_longframe_transactions_tbl.rds")
customer_transactions_tbl |> glimpse()Rows: 460,621
Columns: 4
$ customer_id <chr> "LFC201001_0008", "LFC201001_0004", "LFC201001_0005", "L…
$ tnx_timestamp <dttm> 2010-01-01 04:27:56, 2010-01-01 05:11:45, 2010-01-01 06…
$ invoice_id <chr> "T20100101-0001", "T20100101-0002", "T20100101-0003", "T…
$ tnx_amount <dbl> 17.07, 27.40, 45.36, 367.20, 29.83, 225.63, 20.67, 429.3…1.2 Load Derived Data
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id_1000 <- read_rds("data/longsynth_id_1000.rds")
id_5000 <- read_rds("data/longsynth_id_5000.rds")
id_10000 <- read_rds("data/longsynth_id_10000.rds")
fit_1000_data_tbl <- read_rds("data/longsynth_fit_1000_data_tbl.rds")
fit_10000_data_tbl <- read_rds("data/longsynth_fit_10000_data_tbl.rds")
customer_fit_stats_tbl <- fit_1000_data_tbl
customer_summarystats_tbl <- read_rds("data/longsynth_customer_summarystats_tbl.rds")
obs_fitdata_tbl <- read_rds("data/longsynth_obs_fitdata_tbl.rds")
obs_validdata_tbl <- read_rds("data/longsynth_obs_validdata_tbl.rds")
customer_subset_id <- id_10001.3 Load Subset Data
We also want to construct our data subsets for the purposes of speeding up our valuations.
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customer_fit_subset_tbl <- obs_fitdata_tbl |>
filter(customer_id %in% customer_subset_id)
customer_fit_subset_tbl |> glimpse()Rows: 1,000
Columns: 6
$ customer_id <chr> "LFC201001_0016", "LFC201001_0027", "LFC201001_0094", "…
$ first_tnx_date <dttm> 2010-01-02 04:57:52, 2010-01-03 07:02:01, 2010-01-09 2…
$ last_tnx_date <dttm> 2010-01-02 04:57:52, 2010-03-17 11:30:13, 2010-03-12 1…
$ tnx_count <dbl> 0, 4, 1, 0, 5, 2, 1, 38, 0, 0, 0, 0, 10, 10, 4, 2, 23, …
$ t_x <dbl> 0.0000000, 10.4551784, 8.8501005, 0.0000000, 5.9899357,…
$ T_cal <dbl> 625.9704, 625.8153, 624.8741, 624.7492, 624.7605, 623.8…Show code
customer_valid_subset_tbl <- obs_validdata_tbl |>
filter(customer_id %in% customer_subset_id)
customer_valid_subset_tbl |> glimpse()Rows: 1,000
Columns: 3
$ customer_id <chr> "LFC201001_0016", "LFC201001_0027", "LFC201001_0094"…
$ tnx_count <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ tnx_last_interval <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …We now use these datasets to set the start and end dates for our various validation methods.
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dates_lst <- read_rds("data/longsynth_simulation_dates.rds")
use_fit_start_date <- dates_lst$use_fit_start_date
use_fit_end_date <- dates_lst$use_fit_end_date
use_valid_start_date <- dates_lst$use_valid_start_date
use_valid_end_date <- dates_lst$use_valid_end_dateFinally, we need to set our directories where we save our Stan code and the model outputs.
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stan_modeldir <- "stan_models"
stan_codedir <- "stan_code"2 Fit First Hierarchical Lambda Model
Our first hierarchical model puts a hierarchical prior around the mean of our population \(\lambda\) - lambda_mn.
Once again we use a Gamma prior for it.
2.1 Compile and Fit Stan Model
We now compile this model using CmdStanR.
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pnbd_onehierlambda_stanmodel <- cmdstan_model(
"stan_code/pnbd_onehier_lambda.stan",
include_paths = stan_codedir,
pedantic = TRUE,
dir = stan_modeldir
)We then use this compiled model with our data to produce a fit of the data.
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stan_modelname <- "pnbd_long_onehierlambda1"
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_seed <- stanfit_seed + 1
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
hier_lambda_mn_p1 = 0.25,
hier_lambda_mn_p2 = 1,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 1.00,
)
if(!file_exists(stanfit_object_file)) {
pnbd_long_onehierlambda1_stanfit <- pnbd_onehierlambda_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_long_onehierlambda1_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_long_onehierlambda1_stanfit <- read_rds(stanfit_object_file)
}
pnbd_long_onehierlambda1_stanfit$summary()# A tibble: 3,003 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -2.37e+4 -2.37e+4 34.6 3.58e+1 -2.38e+4 -2.37e+4 1.00 640.
2 lambda_mn 2.44e-1 2.44e-1 0.0101 9.47e-3 2.28e-1 2.61e-1 1.00 2132.
3 lambda[1] 1.37e-1 8.86e-2 0.151 9.33e-2 5.31e-3 4.32e-1 1.00 3011.
4 lambda[2] 2.95e-1 2.69e-1 0.145 1.32e-1 1.07e-1 5.83e-1 1.00 4051.
5 lambda[3] 1.10e-1 8.66e-2 0.0872 6.99e-2 1.66e-2 2.77e-1 1.01 3523.
6 lambda[4] 1.38e-1 8.33e-2 0.162 9.43e-2 5.48e-3 4.67e-1 1.00 3566.
7 lambda[5] 5.14e-1 4.80e-1 0.220 2.06e-1 2.11e-1 9.12e-1 1.01 4417.
8 lambda[6] 2.96e-1 2.56e-1 0.193 1.68e-1 6.66e-2 6.63e-1 1.00 3720.
9 lambda[7] 2.49e-1 1.94e-1 0.204 1.59e-1 3.33e-2 6.33e-1 1.00 3364.
10 lambda[8] 2.97e-1 2.94e-1 0.0466 4.76e-2 2.24e-1 3.79e-1 1.00 5224.
# ℹ 2,993 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
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pnbd_long_onehierlambda1_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda1-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda1-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda1-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda1-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.2.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
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parameter_subset <- c(
"lambda_mn",
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_long_onehierlambda1_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
Show code
pnbd_long_onehierlambda1_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
2.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
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pnbd_stanfit <- pnbd_long_onehierlambda1_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_long_onehierlambda1_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_long_onehierlambda1",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
precompute_rootdir = "precompute",
data_dir = "data",
summary_include_tnx = FALSE,
sim_seed = 4310
)
pnbd_long_onehierlambda1_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_long_onehierlambda1_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_long_onehierlambda1_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_long_onehierlambda1_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_long_onehierlambda1_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_long_onehierlambda1_assess_valid_simstats_tbl.rds"2.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
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simdata_tbl <- pnbd_long_onehierlambda1_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
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insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
2.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
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simdata_tbl <- pnbd_long_onehierlambda1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
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outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
3 Fit Second Hierarchical Lambda Model
In this model, we are going with a broadly similar model but we are instead using a different mean for our hierarchical prior.
3.1 Fit Stan Model
We now want to fit the model to our data using this alternative prior for lambda_mn.
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stan_modelname <- "pnbd_long_onehierlambda2"
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_seed <- stanfit_seed + 1
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
hier_lambda_mn_p1 = 0.50,
hier_lambda_mn_p2 = 1,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 1.00,
)
if(!file_exists(stanfit_object_file)) {
pnbd_long_onehierlambda2_stanfit <- pnbd_onehierlambda_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_long_onehierlambda2_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_long_onehierlambda2_stanfit <- read_rds(stanfit_object_file)
}
pnbd_long_onehierlambda2_stanfit$summary()# A tibble: 3,003 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -2.37e+4 -2.37e+4 34.7 34.2 -2.38e+4 -2.37e+4 1.00 739.
2 lambda_mn 2.45e-1 2.44e-1 0.0101 0.0108 2.29e-1 2.61e-1 1.00 2802.
3 lambda[1] 1.34e-1 8.11e-2 0.165 0.0912 3.81e-3 4.54e-1 1.00 1917.
4 lambda[2] 2.91e-1 2.68e-1 0.136 0.123 1.14e-1 5.44e-1 1.00 3528.
5 lambda[3] 1.13e-1 9.23e-2 0.0899 0.0721 1.70e-2 2.84e-1 1.00 3347.
6 lambda[4] 1.36e-1 8.13e-2 0.158 0.0899 5.08e-3 4.70e-1 1.00 2456.
7 lambda[5] 5.18e-1 4.82e-1 0.231 0.225 2.03e-1 9.42e-1 1.00 4397.
8 lambda[6] 2.94e-1 2.56e-1 0.182 0.157 7.54e-2 6.59e-1 1.00 4058.
9 lambda[7] 2.55e-1 1.99e-1 0.206 0.161 3.55e-2 6.59e-1 1.00 3597.
10 lambda[8] 2.96e-1 2.93e-1 0.0500 0.0496 2.17e-1 3.84e-1 1.00 5815.
# ℹ 2,993 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_long_onehierlambda2_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda2-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda2-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda2-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehierlambda2-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.3.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda_mn",
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_long_onehierlambda2_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
Show code
pnbd_long_onehierlambda2_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
3.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_long_onehierlambda2_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_long_onehierlambda2_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_long_onehierlambda2",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
precompute_rootdir = "precompute",
data_dir = "data",
summary_include_tnx = FALSE,
sim_seed = 4320
)
pnbd_long_onehierlambda2_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_long_onehierlambda2_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_long_onehierlambda2_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_long_onehierlambda2_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_long_onehierlambda2_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_long_onehierlambda2_assess_valid_simstats_tbl.rds"3.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_long_onehierlambda2_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
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insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
3.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_long_onehierlambda1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
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outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
4 Fit First Hierarchical Mu Model
We now construct the same hierarchical model but based around mu_mn.
4.1 Compile and Fit Stan Model
We compile this model using CmdStanR.
Show code
pnbd_onehiermu_stanmodel <- cmdstan_model(
"stan_code/pnbd_onehier_mu.stan",
include_paths = stan_codedir,
pedantic = TRUE,
dir = stan_modeldir
)We then use this compiled model with our data to produce a fit of the data.
Show code
stan_modelname <- "pnbd_long_onehiermu1"
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_seed <- stanfit_seed + 1
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
hier_mu_mn_p1 = 0.50,
hier_mu_mn_p2 = 1.00,
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_cv = 1.00
)
if(!file_exists(stanfit_object_file)) {
pnbd_long_onehiermu1_stanfit <- pnbd_onehiermu_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_long_onehiermu1_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_long_onehiermu1_stanfit <- read_rds(stanfit_object_file)
}
pnbd_long_onehiermu1_stanfit$summary()# A tibble: 3,003 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -2.28e+4 -2.28e+4 3.52e+1 3.61e+1 -2.29e+4 -2.28e+4 1.01 522.
2 mu_mn 1.06e-1 1.06e-1 6.88e-3 6.70e-3 9.53e-2 1.18e-1 1.00 1208.
3 lambda[1] 1.44e-1 8.23e-2 1.78e-1 9.46e-2 4.17e-3 4.86e-1 1.00 2369.
4 lambda[2] 2.94e-1 2.71e-1 1.39e-1 1.32e-1 1.09e-1 5.59e-1 1.00 3620.
5 lambda[3] 1.12e-1 9.18e-2 8.66e-2 7.41e-2 1.60e-2 2.89e-1 1.00 3522.
6 lambda[4] 1.46e-1 8.49e-2 1.87e-1 9.76e-2 3.87e-3 4.98e-1 1.00 3227.
7 lambda[5] 5.20e-1 4.87e-1 2.25e-1 2.07e-1 2.14e-1 9.60e-1 1.00 4728.
8 lambda[6] 2.98e-1 2.53e-1 1.98e-1 1.77e-1 6.47e-2 6.79e-1 1.00 3445.
9 lambda[7] 2.56e-1 2.07e-1 2.07e-1 1.65e-1 3.54e-2 6.64e-1 1.00 3534.
10 lambda[8] 2.96e-1 2.94e-1 4.73e-2 4.64e-2 2.22e-1 3.79e-1 1.00 3526.
# ℹ 2,993 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_long_onehiermu1_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu1-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu1-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu1-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu1-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.4.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"mu_mn",
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_long_onehiermu1_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
Show code
pnbd_long_onehiermu1_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
4.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_long_onehiermu1_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_long_onehiermu1_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_long_onehiermu1",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
precompute_rootdir = "precompute",
data_dir = "data",
summary_include_tnx = FALSE,
sim_seed = 4330
)
pnbd_long_onehiermu1_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_long_onehiermu1_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_long_onehiermu1_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_long_onehiermu1_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_long_onehiermu1_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_long_onehiermu1_assess_valid_simstats_tbl.rds"4.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_long_onehiermu1_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
4.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_long_onehierlambda1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
5 Fit Second Hierarchical Lambda Model
In this model, we are going with a broadly similar model but we are instead using a different mean for our hierarchical prior.
5.1 Fit Stan Model
We now want to fit the model to our data using this alternative prior for lambda_mn.
Show code
stan_modelname <- "pnbd_long_onehiermu2"
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_seed <- stanfit_seed + 1
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
hier_mu_mn_p1 = 0.25,
hier_mu_mn_p2 = 1.00,
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_cv = 1.00
)
if(!file_exists(stanfit_object_file)) {
pnbd_long_onehiermu2_stanfit <- pnbd_onehiermu_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_long_onehiermu2_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_long_onehiermu2_stanfit <- read_rds(stanfit_object_file)
}
pnbd_long_onehiermu2_stanfit$summary()# A tibble: 3,003 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -2.28e+4 -2.28e+4 3.44e+1 3.42e+1 -2.29e+4 -2.28e+4 1.01 581.
2 mu_mn 1.06e-1 1.06e-1 6.78e-3 6.93e-3 9.57e-2 1.18e-1 1.00 1254.
3 lambda[1] 1.41e-1 8.62e-2 1.59e-1 9.71e-2 4.96e-3 4.71e-1 0.999 2359.
4 lambda[2] 2.92e-1 2.64e-1 1.49e-1 1.36e-1 1.00e-1 5.69e-1 1.01 4347.
5 lambda[3] 1.15e-1 9.29e-2 9.11e-2 7.48e-2 1.61e-2 2.99e-1 1.00 4015.
6 lambda[4] 1.46e-1 9.09e-2 1.61e-1 1.01e-1 4.87e-3 4.63e-1 0.999 3354.
7 lambda[5] 5.18e-1 4.83e-1 2.29e-1 2.22e-1 2.12e-1 9.53e-1 1.00 3757.
8 lambda[6] 2.96e-1 2.59e-1 1.82e-1 1.60e-1 7.70e-2 6.47e-1 1.00 5110.
9 lambda[7] 2.53e-1 1.98e-1 2.14e-1 1.59e-1 3.22e-2 6.67e-1 1.00 3275.
10 lambda[8] 2.95e-1 2.92e-1 4.81e-2 4.61e-2 2.22e-1 3.81e-1 1.00 6039.
# ℹ 2,993 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_long_onehiermu2_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu2-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu2-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu2-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_long_onehiermu2-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.5.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"mu_mn",
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_long_onehiermu2_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
Show code
pnbd_long_onehiermu2_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
5.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_long_onehiermu2_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_long_onehiermu2_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_long_onehiermu2",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
precompute_rootdir = "precompute",
data_dir = "data",
summary_include_tnx = FALSE,
sim_seed = 4340
)
pnbd_long_onehiermu2_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_long_onehiermu2_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_long_onehiermu2_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_long_onehiermu2_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_long_onehiermu2_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_long_onehiermu2_assess_valid_simstats_tbl.rds"5.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_long_onehiermu2_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
5.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_long_onehiermu1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
6 Compare Model Outputs
We have looked at each of the models individually, but it is also worth looking at each of the models as a group.
Show code
calculate_simulation_statistics <- function(file_rds) {
simdata_tbl <- read_rds(file_rds)
multicount_cust_tbl <- simdata_tbl |>
filter(sim_tnx_count > 0) |>
count(draw_id, name = "multicust_count")
totaltnx_data_tbl <- simdata_tbl |>
count(draw_id, wt = sim_tnx_count, name = "simtnx_count")
simstats_tbl <- multicount_cust_tbl |>
inner_join(totaltnx_data_tbl, by = "draw_id")
return(simstats_tbl)
}Show code
obs_fit_customer_count <- customer_fit_subset_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_valid_customer_count <- customer_valid_subset_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_fit_total_count <- customer_fit_subset_tbl |>
pull(tnx_count) |>
sum()
obs_valid_total_count <- customer_valid_subset_tbl |>
pull(tnx_count) |>
sum()
obs_stats_tbl <- tribble(
~assess_type, ~name, ~obs_value,
"fit", "multicust_count", obs_fit_customer_count,
"fit", "simtnx_count", obs_fit_total_count,
"valid", "multicust_count", obs_valid_customer_count,
"valid", "simtnx_count", obs_valid_total_count
)
model_assess_tbl <- dir_ls("data", regexp = "pnbd_long_(one|fixed).*_assess_.*simstats") |>
enframe(name = NULL, value = "file_path") |>
filter(str_detect(file_path, "_assess_model_", negate = TRUE)) |>
mutate(
model_label = str_replace(file_path, "data/pnbd_long_(.*?)_assess_.*", "\\1"),
assess_type = if_else(str_detect(file_path, "_assess_fit_"), "fit", "valid"),
sim_data = map(
file_path, calculate_simulation_statistics,
.progress = "calculate_simulation_statistics"
)
)
model_assess_tbl |> glimpse()Rows: 14
Columns: 4
$ file_path <fs::path> "data/pnbd_long_fixed1_assess_fit_simstats_tbl.rds", …
$ model_label <chr> "fixed1", "fixed1", "fixed2", "fixed2", "fixed3", "fixed3"…
$ assess_type <chr> "fit", "valid", "fit", "valid", "fit", "valid", "fit", "va…
$ sim_data <list> [<tbl_df[2000 x 3]>], [<tbl_df[2000 x 3]>], [<tbl_df[2000…Show code
model_assess_summstat_tbl <- model_assess_tbl |>
select(model_label, assess_type, sim_data) |>
unnest(sim_data) |>
pivot_longer(
cols = !c(model_label, assess_type, draw_id)
) |>
group_by(model_label, assess_type, name) |>
summarise(
.groups = "drop",
mean_val = mean(value),
p10 = quantile(value, 0.10),
p25 = quantile(value, 0.25),
p50 = quantile(value, 0.50),
p75 = quantile(value, 0.75),
p90 = quantile(value, 0.90)
)
model_assess_summstat_tbl |> glimpse()Rows: 28
Columns: 9
$ model_label <chr> "fixed1", "fixed1", "fixed1", "fixed1", "fixed2", "fixed2"…
$ assess_type <chr> "fit", "fit", "valid", "valid", "fit", "fit", "valid", "va…
$ name <chr> "multicust_count", "simtnx_count", "multicust_count", "sim…
$ mean_val <dbl> 638.7475, 8382.0800, 51.9680, 537.3570, 700.5110, 4637.715…
$ p10 <dbl> 621.0, 7609.0, 47.0, 472.0, 683.0, 4244.9, 35.0, 288.0, 60…
$ p25 <dbl> 630.00, 7949.75, 49.00, 502.00, 691.00, 4417.00, 37.00, 31…
$ p50 <dbl> 639.0, 8366.5, 52.0, 536.5, 700.5, 4634.0, 39.0, 340.0, 61…
$ p75 <dbl> 648.00, 8785.00, 54.00, 571.00, 710.00, 4837.25, 42.00, 37…
$ p90 <dbl> 656.0, 9176.1, 57.0, 606.0, 719.0, 5050.0, 44.0, 398.0, 63…Show code
#! echo: TRUE
ggplot(model_assess_summstat_tbl) +
geom_errorbar(
aes(x = model_label, ymin = p10, ymax = p90), width = 0
) +
geom_errorbar(
aes(x = model_label, ymin = p25, ymax = p75), width = 0, linewidth = 3
) +
geom_hline(
aes(yintercept = obs_value),
data = obs_stats_tbl, colour = "red"
) +
scale_y_continuous(labels = label_comma()) +
expand_limits(y = 0) +
facet_wrap(
vars(assess_type, name), scale = "free_y"
) +
labs(
x = "Model",
y = "Count",
title = "Comparison Plot for the Different Models"
) +
theme(
axis.text.x = element_text(angle = 20, vjust = 0.5, size = 8)
)
6.1 Write Assessment Data to Disk
We now want to save the assessment data to disk.
Show code
model_assess_tbl |> write_rds("data/assess_data_pnbd_long_onehier_tbl.rds")7 R Environment
Show code
options(width = 120L)
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os Ubuntu 22.04.2 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Dublin
date 2023-08-22
pandoc 2.19.2 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] RSPM (R 4.2.0)
arrayhelpers 1.1-0 2020-02-04 [1] RSPM (R 4.2.0)
backports 1.4.1 2021-12-13 [1] RSPM (R 4.2.0)
base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.2.0)
bayesplot * 1.10.0 2022-11-16 [1] RSPM (R 4.2.0)
bit 4.0.5 2022-11-15 [1] RSPM (R 4.2.0)
bit64 4.0.5 2020-08-30 [1] RSPM (R 4.2.0)
boot 1.3-28.1 2022-11-22 [2] CRAN (R 4.2.3)
bridgesampling 1.1-2 2021-04-16 [1] RSPM (R 4.2.0)
brms * 2.19.0 2023-03-14 [1] RSPM (R 4.2.0)
Brobdingnag 1.2-9 2022-10-19 [1] RSPM (R 4.2.0)
cachem 1.0.7 2023-02-24 [1] RSPM (R 4.2.0)
callr 3.7.3 2022-11-02 [1] RSPM (R 4.2.0)
checkmate 2.1.0 2022-04-21 [1] RSPM (R 4.2.0)
cli 3.6.1 2023-03-23 [1] RSPM (R 4.2.0)
cmdstanr * 0.5.3 2023-07-21 [1] Github (stan-dev/cmdstanr@22b391e)
coda 0.19-4 2020-09-30 [1] RSPM (R 4.2.0)
codetools 0.2-19 2023-02-01 [2] CRAN (R 4.2.3)
colorspace 2.1-0 2023-01-23 [1] RSPM (R 4.2.0)
colourpicker 1.2.0 2022-10-28 [1] RSPM (R 4.2.0)
conflicted * 1.2.0 2023-02-01 [1] RSPM (R 4.2.0)
cowplot * 1.1.1 2020-12-30 [1] RSPM (R 4.2.0)
crayon 1.5.2 2022-09-29 [1] RSPM (R 4.2.0)
crosstalk 1.2.0 2021-11-04 [1] RSPM (R 4.2.0)
digest 0.6.31 2022-12-11 [1] RSPM (R 4.2.0)
directlabels * 2021.1.13 2021-01-16 [1] RSPM (R 4.2.0)
distributional 0.3.2 2023-03-22 [1] RSPM (R 4.2.0)
dplyr * 1.1.1 2023-03-22 [1] RSPM (R 4.2.0)
DT 0.27 2023-01-17 [1] RSPM (R 4.2.0)
dygraphs 1.1.1.6 2018-07-11 [1] RSPM (R 4.2.0)
ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.2.0)
evaluate 0.20 2023-01-17 [1] RSPM (R 4.2.0)
fansi 1.0.4 2023-01-22 [1] RSPM (R 4.2.0)
farver 2.1.1 2022-07-06 [1] RSPM (R 4.2.0)
fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.2.0)
forcats * 1.0.0 2023-01-29 [1] RSPM (R 4.2.0)
fs * 1.6.1 2023-02-06 [1] RSPM (R 4.2.0)
furrr * 0.3.1 2022-08-15 [1] RSPM (R 4.2.0)
future * 1.32.0 2023-03-07 [1] RSPM (R 4.2.0)
gamm4 0.2-6 2020-04-03 [1] RSPM (R 4.2.0)
generics 0.1.3 2022-07-05 [1] RSPM (R 4.2.0)
ggdist 3.2.1 2023-01-18 [1] RSPM (R 4.2.0)
ggplot2 * 3.4.2 2023-04-03 [1] RSPM (R 4.2.0)
globals 0.16.2 2022-11-21 [1] RSPM (R 4.2.0)
glue * 1.6.2 2022-02-24 [1] RSPM (R 4.2.0)
gridExtra 2.3 2017-09-09 [1] RSPM (R 4.2.0)
gtable 0.3.3 2023-03-21 [1] RSPM (R 4.2.0)
gtools 3.9.4 2022-11-27 [1] RSPM (R 4.2.0)
hms 1.1.3 2023-03-21 [1] RSPM (R 4.2.0)
htmltools 0.5.5 2023-03-23 [1] RSPM (R 4.2.0)
htmlwidgets 1.6.2 2023-03-17 [1] RSPM (R 4.2.0)
httpuv 1.6.9 2023-02-14 [1] RSPM (R 4.2.0)
igraph 1.4.2 2023-04-07 [1] RSPM (R 4.2.0)
inline 0.3.19 2021-05-31 [1] RSPM (R 4.2.0)
jsonlite 1.8.4 2022-12-06 [1] RSPM (R 4.2.0)
knitr 1.42 2023-01-25 [1] RSPM (R 4.2.0)
labeling 0.4.2 2020-10-20 [1] RSPM (R 4.2.0)
later 1.3.0 2021-08-18 [1] RSPM (R 4.2.0)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.2.3)
lifecycle 1.0.3 2022-10-07 [1] RSPM (R 4.2.0)
listenv 0.9.0 2022-12-16 [1] RSPM (R 4.2.0)
lme4 1.1-32 2023-03-14 [1] RSPM (R 4.2.0)
loo 2.6.0 2023-03-31 [1] RSPM (R 4.2.0)
lubridate * 1.9.2 2023-02-10 [1] RSPM (R 4.2.0)
magrittr * 2.0.3 2022-03-30 [1] RSPM (R 4.2.0)
markdown 1.6 2023-04-07 [1] RSPM (R 4.2.0)
MASS 7.3-58.2 2023-01-23 [2] CRAN (R 4.2.3)
Matrix 1.5-3 2022-11-11 [2] CRAN (R 4.2.3)
matrixStats 0.63.0 2022-11-18 [1] RSPM (R 4.2.0)
memoise 2.0.1 2021-11-26 [1] RSPM (R 4.2.0)
mgcv 1.8-42 2023-03-02 [2] CRAN (R 4.2.3)
mime 0.12 2021-09-28 [1] RSPM (R 4.2.0)
miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.2.0)
minqa 1.2.5 2022-10-19 [1] RSPM (R 4.2.0)
munsell 0.5.0 2018-06-12 [1] RSPM (R 4.2.0)
mvtnorm 1.1-3 2021-10-08 [1] RSPM (R 4.2.0)
nlme 3.1-162 2023-01-31 [2] CRAN (R 4.2.3)
nloptr 2.0.3 2022-05-26 [1] RSPM (R 4.2.0)
parallelly 1.35.0 2023-03-23 [1] RSPM (R 4.2.0)
pillar 1.9.0 2023-03-22 [1] RSPM (R 4.2.0)
pkgbuild 1.4.0 2022-11-27 [1] RSPM (R 4.2.0)
pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.2.0)
plyr 1.8.8 2022-11-11 [1] RSPM (R 4.2.0)
posterior * 1.4.1 2023-03-14 [1] RSPM (R 4.2.0)
prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.2.0)
processx 3.8.1 2023-04-18 [1] RSPM (R 4.2.0)
projpred 2.5.0 2023-04-05 [1] RSPM (R 4.2.0)
promises 1.2.0.1 2021-02-11 [1] RSPM (R 4.2.0)
ps 1.7.5 2023-04-18 [1] RSPM (R 4.2.0)
purrr * 1.0.1 2023-01-10 [1] RSPM (R 4.2.0)
quadprog 1.5-8 2019-11-20 [1] RSPM (R 4.2.0)
R6 2.5.1 2021-08-19 [1] RSPM (R 4.2.0)
Rcpp * 1.0.10 2023-01-22 [1] RSPM (R 4.2.0)
RcppParallel 5.1.7 2023-02-27 [1] RSPM (R 4.2.0)
readr * 2.1.4 2023-02-10 [1] RSPM (R 4.2.0)
reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.2.0)
rlang * 1.1.0 2023-03-14 [1] RSPM (R 4.2.0)
rmarkdown 2.21 2023-03-26 [1] RSPM (R 4.2.0)
rstan 2.21.8 2023-01-17 [1] RSPM (R 4.2.0)
rstantools 2.3.1 2023-03-30 [1] RSPM (R 4.2.0)
rsyslog * 1.0.2 2021-06-04 [1] RSPM (R 4.2.0)
scales * 1.2.1 2022-08-20 [1] RSPM (R 4.2.0)
sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.2.0)
shiny 1.7.4 2022-12-15 [1] RSPM (R 4.2.0)
shinyjs 2.1.0 2021-12-23 [1] RSPM (R 4.2.0)
shinystan 2.6.0 2022-03-03 [1] RSPM (R 4.2.0)
shinythemes 1.2.0 2021-01-25 [1] RSPM (R 4.2.0)
StanHeaders 2.21.0-7 2020-12-17 [1] RSPM (R 4.2.0)
stringi 1.7.12 2023-01-11 [1] RSPM (R 4.2.0)
stringr * 1.5.0 2022-12-02 [1] RSPM (R 4.2.0)
svUnit 1.0.6 2021-04-19 [1] RSPM (R 4.2.0)
tensorA 0.36.2 2020-11-19 [1] RSPM (R 4.2.0)
threejs 0.3.3 2020-01-21 [1] RSPM (R 4.2.0)
tibble * 3.2.1 2023-03-20 [1] RSPM (R 4.2.0)
tidybayes * 3.0.4 2023-03-14 [1] RSPM (R 4.2.0)
tidyr * 1.3.0 2023-01-24 [1] RSPM (R 4.2.0)
tidyselect 1.2.0 2022-10-10 [1] RSPM (R 4.2.0)
tidyverse * 2.0.0 2023-02-22 [1] RSPM (R 4.2.0)
timechange 0.2.0 2023-01-11 [1] RSPM (R 4.2.0)
tzdb 0.3.0 2022-03-28 [1] RSPM (R 4.2.0)
utf8 1.2.3 2023-01-31 [1] RSPM (R 4.2.0)
vctrs 0.6.2 2023-04-19 [1] RSPM (R 4.2.0)
vroom 1.6.1 2023-01-22 [1] RSPM (R 4.2.0)
withr 2.5.0 2022-03-03 [1] RSPM (R 4.2.0)
xfun 0.38 2023-03-24 [1] RSPM (R 4.2.0)
xtable 1.8-4 2019-04-21 [1] RSPM (R 4.2.0)
xts 0.13.1 2023-04-16 [1] RSPM (R 4.2.0)
yaml 2.3.7 2023-01-23 [1] RSPM (R 4.2.0)
zoo 1.8-12 2023-04-13 [1] RSPM (R 4.2.0)
[1] /usr/local/lib/R/site-library
[2] /usr/local/lib/R/library
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options(width = 80L)